Answers in context
The agent knows which page the visitor is on and responds around that moment.
AIA.Works | Auckland solo AI studio
AIA.Works is an independent solo studio founded by Cameron Hynes in Auckland, NZ. It proves a simple idea: one person with AI-native systems can run company-scale execution.
AI systems for real business bottlenecks.
I help teams turn painful workflows into practical AI tools. Try the agents, see what the first prototype costs, and decide whether one workflow is worth building.
People
AI-enable your team
Train your people to use AI safely on the work they already do.
Role-specific workflows, reusable prompts, safe usage rules, and practical habits.
Strategy
Plan your AI moves
Get a clear view of where AI belongs, what to avoid, and what to test first.
A sharper priority map for tools, vendors, risks, and first workflow candidates.
Build
Transform one workflow
Point at one painful bottleneck and turn it into a working AI prototype.
A useful version on real business context before a larger system build.
Live agent demo
Try chat. Try voice. Then see the next step.
Ask about a workflow, pricing, or AI demos. This is the kind of customer-facing or staff-facing agent AIA can build around your work.
Live sales agent
Chat with the AIA website agent
Ask what AIA can build, compare examples, or describe one workflow. The agent turns the conversation into clearer next steps your team can review.
I am the live website chat agent. Ask what AIA can build, ask for examples, or describe one workflow you want AI to help with.
Browser voice agent
Talk through one workflow.
Browser mic, xAI Realtime voice, live transcript, and a human-gated handoff. Talk through the workflow, then a human reviews the next step.
Agent capabilities
What the website agent can do.
The live agent is a small version of the customer and staff agents AIA can build into your own workflow.
Routes to the right next step
It can move a visitor toward demos, pricing, voice, or a workflow handoff.
Carries the workflow forward
Useful chat context can travel into the enquiry form so the visitor does not repeat themselves.
Keeps control human-reviewed
The agent can shape the next step, but business action still goes through a person.
Commercial clarity
Start small, then build what earns it.
Typical first step
Prototype
From $2,500
A working AI prototype on your business context before live integration.
3-8 weeks
Integration / Build
From $15,000
AI built into the workflow with APIs, databases, permissions, and handover.
After build handover
Ongoing support
$1,500/month
Backend support and maintenance for production AI integrations.
AI demos
What this can become.
These are only a few of the AI demos and workflow prototypes AIA has built. Open the demo page to see more finance, document, market, data, retail, and agent demos.
Finance
AI finance dashboard
Lets finance users ask plain-English questions and changes the dashboard view around the answer.
Open live demo ->Documents
AI document studio
Combines drafting, PDF export, branding, and signing in one browser workflow.
Open live demo ->Market intelligence
AI market intelligence scanner
Scans job listings and company signals, then turns them into a reviewable market dashboard.
Open live demo ->Research demo
AI media framing monitor
Reviews coverage across sources and highlights how the framing differs.
Open live demo ->Live demo
Chat with your data
Turns a dataset into a chat interface that can answer questions with SQL-backed results.
Open live demo ->Experimental
AI interactive game engine
Uses AI as the logic layer for an interactive experience, not just as a text generator.
Open live demo ->Case studies
Anonymised work, real operating problems.
These are the kinds of client workflows AIA has been building: legacy systems, finance decisions, content operations, and project intelligence.
Transport and equipment SaaS
AI-enabled fleet diagnostics
- Bottleneck
- Service and unit context lived across a complex legacy database, making technician-ready insight hard to reach.
- Built
- An intelligent diagnostics layer with unit history, repeat-fault review, and data-backed versus estimated labels.
- Control
- Every insight is labelled so users can see what came from source data and what was estimated.
- Why it matters
- Turns buried service history into faster workshop decisions without replacing technician judgement.
Equipment finance and leasing
AI credit decision pipeline
- Bottleneck
- A credit manager had to manually inspect application packs and repeat the same decision checks under time pressure.
- Built
- A 4-stage assessment workflow mapped to 16 existing credit checks, producing reviewable recommendations and risk notes.
- Control
- The AI recommends with source traceability; the human remains the final decision maker.
- Why it matters
- Moves assessment work toward minutes while preserving auditability and lending control.
Construction intelligence SaaS
AI project health analysis
- Bottleneck
- Large project datasets could show margin erosion and delivery risk, but the useful signals were buried across schemas.
- Built
- Real-data analysis pipelines that produce structured project health assessments and lineage back to source fields.
- Control
- Numbers and claims are tied back to source tables and reviewable analysis steps.
- Why it matters
- Turns project data into early warning signals leadership can act on.
Insurance brokerage
AI content factory
- Bottleneck
- Brand assets existed, but turning them into consistent multi-channel social content was slow and manual.
- Built
- A content workflow that generates platform-specific creative assets and publishes across social channels.
- Control
- Scheduling, review, publishing, and synced post management stay visible to the business.
- Why it matters
- Creates a repeatable marketing operation without hiring a larger content team.
Controls
Useful AI without losing control.
The goal is not blind automation. The useful version starts small, shows its work, and leaves final business action reviewable.
Human final say
AI can draft, recommend, summarise, and route work.
Business decisions stay reviewable and human-approved before anything material moves.
Source-labelled outputs
Useful AI work depends on knowing where claims come from.
Outputs distinguish source-backed facts, calculated fields, and AI-estimated interpretation.
Small test before integration
Large integrations are expensive when the workflow is still unclear.
The first build proves the task, output, and review point before live-system work begins.
A good first workflow has...
There is a clear workflow where time, quality, or follow-up is leaking.
A realistic sample or export can prove the task before live integration.
A human review point can stay in place before anything material happens.
Start with one workflow.
Tell the agent where the bottleneck is, or send a short brief and start with the smallest useful prototype.
Send a workflow